Continuous casting round billet ultrasonic signal time-frequency feature extraction method, system and equipment and storage medium

Abstract

The invention provides a continuous casting round billet ultrasonic signal time-frequency feature extraction method, system and device and a storage medium, and belongs to the technical field of metal material nondestructive testing signal processing and analysis, sampling points are selected on a continuous casting round billet, ultrasonic signals at the sampling points are collected, and a CSV file is generated for each sampling point; inputting the CSV file into MATLAB software, and carrying out denoising and normalization preprocessing on the ultrasonic signal; performing time domain feature extraction and frequency domain feature extraction on the preprocessed ultrasonic signal to obtain a time domain feature extraction result and a frequency domain feature extraction result; and generating a characteristic value trend chart of the time domain characteristic extraction result and the frequency domain characteristic extraction result by using MATLAB so as to visualize the analysis result. Through characteristic analysis of the ultrasonic waveform signals, whether defects exist in the continuous casting round billet or not is judged more comprehensively.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal processing and analysis for non-destructive testing of metal materials, and particularly relates to a method, system, device and storage medium for extracting time-frequency characteristics of ultrasonic signals of continuous casting round billets. Background Art

[0002] In the modern metal material processing industry, high-quality special steel billets are widely used in many key fields, such as high-end machinery manufacturing, aerospace, core components of the automotive industry, etc. These fields have extremely strict requirements for the service performance of the steel billets, and even minor defects in the internal quality of the steel billets may cause serious safety hazards or lead to a significant decline in product performance.

[0003] At the current stage, for the non-destructive testing of the internal quality of continuous casting round billets, it mainly relies on the ultrasonic waveforms captured by flaw detection equipment. However, in the actual operation process, relying solely on the intuitive analysis of the waveforms to evaluate the internal quality of continuous casting round billets has certain limitations. This single analysis method is prone to judgment errors and limits the comprehensiveness and reliability of the detection results.

[0004] Therefore, the present invention provides a method, system, device and storage medium for extracting time-frequency characteristics of ultrasonic signals of continuous casting round billets. Summary of the Invention

[0005] The present invention provides a method, system, device and storage medium for extracting time-frequency characteristics of ultrasonic signals of continuous casting round billets, so as to at least solve the problem that the single analysis method in the prior art is prone to judgment errors and limits the comprehensiveness and reliability of the detection results.

[0006] In a first aspect, an embodiment of the present application provides a method for extracting time-frequency characteristics of ultrasonic signals of continuous casting round billets, and the method includes: Select sampling points on the continuous casting round billet, collect ultrasonic signals at the sampling points, and generate a CSV file for each sampling point; Input the CSV file into MATLAB software to perform denoising and normalization preprocessing on the ultrasonic signals; Perform time-domain feature extraction and frequency-domain feature extraction on the preprocessed ultrasonic signals to obtain time-domain feature extraction results and frequency-domain feature extraction results; Use MATLAB to generate eigenvalue trend graphs of the time-domain feature extraction results and the frequency-domain feature extraction results for visual analysis of the results.

[0007] Further, selecting sampling points on the continuous casting round billet and collecting ultrasonic signals at the sampling points includes: Transmit ultrasonic waves to the continuous casting round billet through an ultrasonic generator; Install a number of ultrasonic probes circumferentially on the continuous casting round billet, and connect the ultrasonic probes to an ultrasonic generator; The ultrasonic probes collect ultrasonic signals reflected by the continuous casting round billet.

[0008] Further, before collecting the ultrasonic signals of the continuous casting round billet, it further includes: Circumferentially along the continuous casting round billet, every Install an ultrasonic probe; Adjust the parameters of the ultrasonic probe, and the parameters include: probe frequency, sampling depth, transmission voltage, number of sampling points, sampling frequency, calibration sound speed, probe delay.

[0009] Further, the CSV file includes the following data: Sampling time interval T, sampling interval distance S, number of sampling points P, amplitude L1.

[0010] Further, perform denoising and normalization preprocessing on the ultrasonic signal, including the following specific steps: Modulate the ultrasonic signal with a chirp signal: (1) In the formula, represents the ultrasonic signal, represents the modulated ultrasonic signal, represents the imaginary part of the complex number, represents the Fourier rotation time, represents pi, represents the Fourier rotation angle; Convolve the modulated ultrasonic signal with another chirp signal: (2) In the formula, represents the first-order time-domain signal, represents the first-order convolution time-domain signal; Use the chirp signal again to Modulate: (3) In the formula, represents the normalized time-domain signal.

[0011] Further, before performing time-domain feature extraction and frequency-domain feature extraction on the preprocessed ultrasonic signal, it further includes: Define the features of the time domain and the frequency domain.

[0012] Further, perform time-domain feature extraction and frequency-domain feature extraction on the preprocessed ultrasonic signal to obtain the time-domain feature extraction result and the frequency-domain feature extraction result, including: Time-domain feature extraction: Use a loop statement to traverse all samples and extract time-domain feature values, where the time-domain feature values include: mean value, standard deviation, skewness, kurtosis, maximum value, minimum value, peak-to-peak value, root mean square, amplitude factor, waveform factor, impact factor, energy; Generate a features array containing the time-domain feature values; Frequency-domain feature extraction: Convert the time-domain sample data to the frequency domain through fast Fourier transform; Define frequency-domain feature values according to a preset frequency-domain feature value definition method; Complete the data analysis of the frequency-domain feature values according to a preset analysis method, and generate a frequency-domain feature value array.

[0013] In a second aspect, an embodiment of the present application further provides an ultrasonic signal time-frequency feature extraction system applied to the ultrasonic signal time-frequency feature extraction method described in the above aspects. The system includes: Sampling module: used to select sampling points on the continuous casting round billet, collect ultrasonic signals at the sampling points, and generate a CSV file for each sampling point; Processing module: used to input the CSV file into MATLAB software and perform denoising and normalization preprocessing on the ultrasonic signal; Analysis module: used to perform time-domain feature extraction and frequency-domain feature extraction on the preprocessed ultrasonic signal to obtain time-domain feature extraction results and frequency-domain feature extraction results; Display module: use MATLAB to generate a feature value trend chart of the time-domain feature extraction results and the frequency-domain feature extraction results to visually analyze the results.

[0014] In a third aspect, an electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps of the ultrasonic signal time-frequency feature extraction method described in the above aspects.

[0015] In a fourth aspect, a storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the ultrasonic signal time-frequency feature extraction method described in the above aspects.

[0016] From the above technical solutions, it can be seen that the present invention has the following advantages: In the ultrasonic signal time-frequency feature extraction method provided by this application, the ultrasonic signal is preprocessed by denoising and normalization, and the time-domain features and frequency-domain features of the preprocessed ultrasonic signal are extracted to obtain the time-domain feature extraction result and the frequency-domain feature extraction result; MATLAB is used to generate the eigenvalue trend graphs of the time-domain feature extraction result and the frequency-domain feature extraction result to visually analyze the results. By observing the time-domain and frequency-domain eigenvalue trend graphs, the eigenvalue differences between different samples are analyzed.

[0017] More accurate analysis in the frequency domain is achieved through Fourier transform, providing a basis for the subsequent analysis of the internal defect data of the continuous casting round billet. These eigenvalue differences are used to determine whether there are defects inside the continuous casting round billet and the related characteristics of the defects.

[0018] Through equipment parameter setting, data acquisition, MATLAB data preprocessing, time-domain and frequency-domain feature extraction and analysis, the present invention realizes the feature extraction of the ultrasonic waveform signal of the sampling sample. Through the feature analysis of the ultrasonic waveform signal, it is possible to more comprehensively determine whether there are defects inside the continuous casting round billet. Brief Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions of the present invention, the accompanying drawings required for description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic diagram of the circumferential sampling points of the continuous casting round billet for the ultrasonic signal time-frequency feature extraction method of the present invention.

[0021] Figure 2 It is the time-domain spectrum of the sampling waveform for the ultrasonic signal time-frequency feature extraction method of the present invention for the continuous casting round billet.

[0022] Figure 3 It is the frequency-domain spectrum of the sampling waveform for the ultrasonic signal time-frequency feature extraction method of the present invention for the continuous casting round billet.

[0023] Figure 4 It is the trend graph of the average value for the ultrasonic signal time-frequency feature extraction method of the present invention for the continuous casting round billet.

[0024] Figure 5 It is the trend graph of the standard deviation for the ultrasonic signal time-frequency feature extraction method of the present invention for the continuous casting round billet.

[0025] Figure 6 It is the trend graph of the skewness for the ultrasonic signal time-frequency feature extraction method of the present invention for the continuous casting round billet.

[0026] Figure 7It is the trend graph of the kurtosis of the time-frequency feature extraction method for the ultrasonic signal of the continuously cast round billet of the present invention.

[0027] Figure 8 It is the trend graph of the maximum value of the time-frequency feature extraction method for the ultrasonic signal of the continuously cast round billet of the present invention.

[0028] Figure 9 It is the trend graph of the minimum value of the time-frequency feature extraction method for the ultrasonic signal of the continuously cast round billet of the present invention.

[0029] Figure 10 It is the trend graph of the peak-to-peak value of the time-frequency feature extraction method for the ultrasonic signal of the continuously cast round billet of the present invention.

[0030] Figure 11 It is the trend graph of the root mean square of the time-frequency feature extraction method for the ultrasonic signal of the continuously cast round billet of the present invention.

[0031] Figure 12 It is the trend graph of the energy of the time-frequency feature extraction method for the ultrasonic signal of the continuously cast round billet of the present invention.

[0032] Figure 13 It is the trend graph of the amplitude factor of the time-frequency feature extraction method for the ultrasonic signal of the continuously cast round billet of the present invention.

[0033] Figure 14 It is the trend graph of the waveform factor of the time-frequency feature extraction method for the ultrasonic signal of the continuously cast round billet of the present invention.

[0034] Figure 15 It is the trend graph of the impact factor of the time-frequency feature extraction method for the ultrasonic signal of the continuously cast round billet of the present invention. Detailed implementation manners

[0035] In the following, the time-frequency feature extraction method, system, device and storage medium of the continuously cast round billet ultrasonic signal will be described in detail, and various embodiments of the present disclosure will be described more comprehensively. The present disclosure can have various embodiments, and adjustments and changes can be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents and / or alternative solutions falling within the spirit and scope of the various embodiments of the present disclosure.

[0036] Hereinafter, the term "comprising" or "may comprise" that may be used in various embodiments of the present disclosure indicates the presence of the disclosed functions, operations, or elements, and does not limit the addition of one or more functions, operations, or elements. Further, as used in various embodiments of the present disclosure, the terms "comprising", "having", and their cognates are only intended to indicate a specific feature, number, step, operation, element, component, or combination of the foregoing items, and should not be construed as precluding the presence or addition of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing items first.

[0037] In various embodiments of the present disclosure, the expression "or" or "at least one of A or / and B" includes any combination or all combinations of the recited words. For example, the expression "A or B" or "at least one of A or / and B" may include A, may include B, or may include both A and B.

[0038] Expressions (such as "first", "second", etc.) used in various embodiments of the present disclosure may modify various constituent elements in the various embodiments, but do not limit the corresponding constituent elements. For example, the above expressions do not limit the order and / or importance of the elements. The above expressions are only for the purpose of distinguishing one element from other elements. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, without departing from the scope of the various embodiments of the present disclosure, the first element may be referred to as the second element, and similarly, the second element may also be referred to as the first element.

[0039] It should be noted that: if it is described that one constituent element is "connected" to another constituent element, the first constituent element may be directly connected to the second constituent element, and a third constituent element may be "connected" between the first constituent element and the second constituent element. Conversely, when one constituent element is "directly connected" to another constituent element, it can be understood that there is no third constituent element between the first constituent element and the second constituent element.

[0040] The term "user" used in various embodiments of the present disclosure may indicate a person using an electronic device, which may be a monitoring person, a testing person, or an operating person.

[0041] Hereinafter, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] The embodiment of the present application provides a method for extracting time-frequency characteristics of ultrasonic signals of continuous casting round billets, which solves the technical problem that there is an urgent need for a single analysis method at present, which is likely to cause deviation in judgment and limits the comprehensiveness and reliability of detection results.

[0043] The following details the technical solutions proposed in the embodiments of the present application with the help of drawings.

[0044] A method for extracting time-frequency characteristics of ultrasonic signals of continuous casting round billets provided by the embodiment of the present application specifically includes the following steps: Select sampling points on the continuous casting round billet, collect ultrasonic signals at the sampling points, and generate a CSV file for each sampling point.

[0045] Input the CSV file into MATLAB software to perform denoising and normalization preprocessing on the ultrasonic signals, which can improve the data quality for subsequent analysis.

[0046] Extract time-domain characteristics and frequency-domain characteristics from the preprocessed ultrasonic signals to obtain time-domain characteristic extraction results and frequency-domain characteristic extraction results.

[0047] Use MATLAB to generate eigenvalue trend graphs of the time-domain characteristic extraction results and frequency-domain characteristic extraction results to visually analyze the results. Observe the time-domain and frequency-domain eigenvalue trend graphs and analyze the eigenvalue differences between different samples. Through the characteristic analysis of the ultrasonic waveform signals, more comprehensively judge whether there are defects inside the continuous casting round billet.

[0048] Since the array range in the frequency domain is relatively smaller, more accurate analysis in the frequency domain is achieved through Fourier transform, providing a basis for subsequent internal defect data analysis. For example, use these eigenvalue differences to judge whether there are defects inside the continuous casting round billet and the related characteristics of the defects.

[0049] In summary, through a series of steps such as equipment parameter setting, data collection, MATLAB data preprocessing, time-domain and frequency-domain characteristic extraction and analysis, this method realizes the extraction of the characteristics of the ultrasonic waveform signals of the sampling samples, and through the characteristic analysis of the ultrasonic waveform signals, more comprehensively judges whether there are defects inside the continuous casting round billet.

[0050] In an exemplary embodiment, selecting sampling points on the continuous casting round billet and collecting ultrasonic signals at the sampling points includes: Emit ultrasonic waves to the continuous casting round billet through an ultrasonic generator.

[0051] Install a number of ultrasonic probes circumferentially on the continuous casting round billet, and the ultrasonic probes are connected to the ultrasonic generator. It should be noted that one of the ultrasonic probes is installed at each sampling point, that is, each ultrasonic probe corresponds to one sampling point.

[0052] The ultrasonic probe collects the ultrasonic signals reflected by the continuous casting round billet.

[0053] According to another embodiment of the present invention, before collecting the ultrasonic signals of the continuous casting round billet, it further includes: Along the circumferential direction of the continuous casting round billet, every An ultrasonic probe is installed.

[0054] Adjust the parameters of the ultrasonic probe, and the parameters include: probe frequency, sampling depth, transmit voltage, number of sampling points, sampling frequency, calibration sound speed, probe delay.

[0055] According to the embodiment of the present application, the CSV file includes the following data: sampling time interval T, sampling interval distance S, number of sampling points P, amplitude L1. Along the circumferential direction of the continuous casting round billet, four points are sampled at equal intervals, and a CSV file is generated for each sampling point. The CSV file is used to record the data of each sampling point, including the sampling time interval T, sampling interval distance S, number of sampling points P, and amplitude L1.

[0056] As shown in Table 1, they respectively represent the sampling time interval T, sampling interval distance S, number of sampling points P, and amplitude L1 of a certain point.

[0057] Table 1 Partial sampling data of the CSV file of a certain point of the continuous casting round billet

[0058] In one embodiment, denoising and normalization preprocessing are performed on the ultrasonic signals, including the following specific steps: Modulate the ultrasonic signals with a chirp signal: (1) In the formula, represents the ultrasonic signal, represents the modulated ultrasonic signal, represents the imaginary part of the complex number, represents the Fourier rotation time, represents pi, represents the Fourier rotation angle, and its value is equal to , is the Fourier rotation order.

[0059] Convolve the modulated ultrasonic signal with another chirp signal: (2) In the formula, represents the first-order time-domain signal, represents the first-order convolution time-domain signal.

[0060] Reusing the chirp signal to perform modulation: (3) where represents the normalized time-domain signal.

[0061] As an example, before extracting the time-domain features and frequency-domain features of the preprocessed ultrasonic signal, it also includes: defining the features in the time domain and frequency domain. Each sample is defined as a two-dimensional sequence, and each sample is a 4*8000 matrix, representing 4 sampling points, and each sampling point has 8000 data points.

[0062] Using MATLAB, define the basic parameters of a single sample, and the basic parameters include: sampling length, acquisition duration, sampling frequency, and number of samples.

[0063] After defining the basic parameters, then use a loop statement to define the time-frequency domain feature values, and the number of loops is the number of samples, as shown in Table 2.

[0064] Table 2 Feature Definition Loop Algorithm Structure

[0065] Run the program to generate the features array. The first row represents all the sample features of the first sample, including 12 features. There are a total of four rows, that is, four data samples are analyzed, as shown in Table 3.

[0066] Table 3 Time-Domain Feature features Array .

[0067] Generate a trend chart of the feature values of four samples through MATLAB, combined with Figure 4-15 , it is found that the four time-domain feature value parameters of standard deviation, root mean square, energy, and waveform factor show good monotonic increasing or decreasing trend characteristics, which are more conducive to subsequent internal defect data analysis.

[0068] It should be further noted that the sampling length of a single sample is determined by the number of sampling points in the CSV file. For example, in this embodiment, the sampling length is 8000. The acquisition duration is determined by the sampling time interval T data of the CSV file. The sampling frequency is determined when setting the parameters of the detection device. In this embodiment, the number of samples is 4, that is, the four sampling points collected along the circumferential direction of the continuous casting round billet correspond to four samples.

[0069] It should be further noted that extracting the time-domain features and frequency-domain features of the preprocessed ultrasonic signal to obtain the time-domain feature extraction result and the frequency-domain feature extraction result includes: Time-domain feature extraction: Use a loop statement to iterate through all samples and extract time-domain feature values. The time-domain feature values include: mean, standard deviation, skewness, kurtosis, maximum value, minimum value, peak-to-peak value, root mean square, amplitude factor, waveform factor, impulse factor, and energy.

[0070] Generate a features array containing the time-domain feature values.

[0071] Among them, the number of loop iterations is the number of samples. Use a loop statement to define the time-domain feature values and extract the time-domain feature values: mean, standard deviation, skewness, kurtosis, maximum value, minimum value, peak-to-peak value, root mean square, amplitude factor, waveform factor, impulse factor, and energy. The time-domain feature values of each sample form a row, and there are four rows corresponding to four samples, generating a features array containing the time-domain feature values. The first row represents all the time-domain sample features of the first sample, with a total of 12 feature values. Analyze the trend graph of the time-domain feature values and find that the feature values show a good monotonic increasing or decreasing trend, which is beneficial to the analysis of internal defect data.

[0072] Among them, the mean refers to the average level of the ultrasonic signal intensity, and the calculation formula is: ; The standard deviation refers to the stability of the ultrasonic signal, and the calculation formula is: ; The skewness refers to the symmetry of the signal distribution, and the calculation formula is: ; The kurtosis refers to the shape of the signal distribution, and the calculation formula is: ; The maximum value refers to the peak amplitude of the signal; The minimum value refers to the valley amplitude of the signal; The peak-to-peak value refers to the entire amplitude range of the signal, and the calculation formula is: ; The root mean square refers to the energy level of the signal, and the calculation formula is: ; The energy refers to the distribution of the signal in different frequency components, and the calculation formula is: ; The amplitude factor refers to the intensity level of the signal, and the calculation formula is: ; The waveform factor refers to the flatness or sharpness of the signal, and the calculation formula is: ; Among them, represents the signal amplitude, which is equal to the height of the sampled data points of the time-domain signal in the time domain and equal to the modulus of the corresponding complex number of the frequency-domain signal in the frequency domain. represents the number of sampling points; The impact factor refers to the transient or impact characteristics in the signal, and the calculation formula is: .

[0073] Frequency-domain feature extraction: Convert the time-domain sample data to the frequency domain through the fast Fourier transform. Perform the fast Fourier transform on the preprocessed ultrasonic signal to obtain the time-frequency domain spectrum of the preprocessed ultrasonic signal.

[0074] Define the frequency-domain feature values according to the preset frequency-domain feature value definition method.

[0075] Complete the data analysis of the frequency-domain feature values according to the preset analysis method to generate an array of frequency-domain feature values. Similarly, the first row represents all the frequency-domain feature values of the first sample. The frequency-domain feature values include the mean, standard deviation, skewness, kurtosis, maximum value, minimum value, peak-to-peak value, root mean square, amplitude factor, waveform factor, impact factor, and energy. The four rows correspond to four samples. The preset frequency-domain feature value definition method is consistent with the time-domain feature definition formula, where it is equal to the height of the sampled data points of the time-domain signal in the time domain and equal to the modulus of the corresponding complex number of the frequency-domain signal in the frequency domain. Generate a frequency-domain feature array, and the features array contains 12 features. Compare the time-domain and frequency-domain features and find that the frequency-domain feature range is smaller, which is more conducive to accurate analysis and discovery of differences between samples.

[0076] The present invention also provides a time-frequency feature extraction system for ultrasonic signals of continuous casting round billets, and the system includes: Sampling module: used to select sampling points on the continuous casting round billet, collect ultrasonic signals at the sampling points, and generate a CSV file for each sampling point.

[0077] Processing module: used to input the CSV file into MATLAB software, perform denoising and normalization preprocessing on the ultrasonic signal, which can improve the data quality for subsequent analysis.

[0078] Analysis module: used to perform time-domain feature extraction and frequency-domain feature extraction on the preprocessed ultrasonic signal to obtain the time-domain feature extraction result and the frequency-domain feature extraction result.

[0079] Display module: use MATLAB to generate a feature value trend graph of the time-domain feature extraction result and the frequency-domain feature extraction result to visually analyze the results.

[0080] The above is an embodiment of the system for extracting time-frequency characteristics of ultrasonic signals of continuously cast round billets provided by the present disclosure. The system for extracting time-frequency characteristics of ultrasonic signals of continuously cast round billets and the method for extracting time-frequency characteristics of ultrasonic signals of continuously cast round billets in the above embodiments belong to the same inventive concept. For the details not described in detail in the embodiment of the system for extracting time-frequency characteristics of ultrasonic signals of continuously cast round billets, reference may be made to the embodiment of the method for extracting time-frequency characteristics of ultrasonic signals of continuously cast round billets.

[0081] The method for extracting time-frequency characteristics of ultrasonic signals of continuously cast round billets provided by the embodiments of the present application can be applied to electronic devices. Those skilled in the art can understand that the structure of the electronic device involved in the embodiments of the present invention does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. In the embodiments of the present invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or claimed herein.

[0082] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, keys, a camera, a display screen, and a SIM card interface, etc.

[0083] It can be understood that the structure schematically shown in the embodiments of the present application does not constitute a specific limitation on the electronic device. In other embodiments of the present application, the electronic device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or have different component arrangements. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0084] The processor may include one or more processing units. For example, the processor may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processors.

[0085] Among them, the processor may be the nerve center and command center of the electronic device. The controller may generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching and executing instructions.

[0086] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can save the instructions or data that the processor has just used or recycled. If the processor needs to use the instruction or data again, it can directly call it from this memory. This avoids repeated accesses, reduces the waiting time of the processor, and thus improves the system efficiency.

[0087] The external memory interface can be used to connect an external memory card, such as a MicroSD card, to expand the storage capacity of the electronic device. The external memory card communicates with the processor through the external memory interface to implement the data storage function. For example, files such as music and videos are saved in the external memory card.

[0088] The internal memory can be used to store computer-executable program code, and the computer-executable program code includes instructions. The processor executes various functional applications and data processing of the electronic device by running the instructions stored in the internal memory. The internal memory may include a program storage area and a data storage area. The internal memory may include a high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0089] The wireless communication function of the electronic device can be implemented through an antenna, a wireless communication module, a modem processor, a baseband processor, etc.

[0090] The wireless communication module can provide solutions for wireless communications applied to electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite systems (GNSSs), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0091] The electronic device can implement audio functions through an audio module, speakers, receivers, microphones, headphone jacks, application processors, etc.

[0092] The electronic device can implement shooting functions through an ISP, a camera, a video codec, a GPU, a display screen, an application processor, etc.

[0093] The electronic device can implement display functions through a GPU, a display screen, an application processor, etc.

[0094] The GPU is a microprocessor for image processing, connecting the display screen and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor may include one or more GPUs, which execute program instructions to generate or change display information.

[0095] The display screen is used to display images, videos, etc. The display screen includes a display panel.

[0096] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of devices, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. Exemplarily, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0098] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the couplings or direct couplings or communication connections shown or discussed among each other may be indirect couplings or communication connections through some interfaces, devices, or units, and may also be electrical, mechanical, or other forms of connection.

[0099] Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present invention. However, those skilled in the art will realize that the technical solutions of the present invention may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be employed. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present invention.

[0100] The above electronic device implements the time-frequency feature extraction method of the ultrasonic signal of the continuous casting round billet in the present application. Sampling points are selected on the continuous casting round billet, ultrasonic signals at the sampling points are collected, and a CSV file is generated for each sampling point; the CSV file is input into MATLAB software, and the ultrasonic signal is preprocessed by denoising and normalization; time-domain feature extraction and frequency-domain feature extraction are performed on the preprocessed ultrasonic signal to obtain the time-domain feature extraction result and the frequency-domain feature extraction result; MATLAB is used to generate an eigenvalue trend graph of the time-domain feature extraction result and the frequency-domain feature extraction result to visually analyze the result. By observing the eigenvalue trend graphs in the time domain and frequency domain, the eigenvalue differences between different samples are analyzed. More accurate analysis in the frequency domain is achieved through Fourier transform, providing a basis for subsequent internal defect data analysis, such as using these eigenvalue differences to determine whether there are defects inside the continuous casting round billet and the related characteristics of the defects.

[0101] In the storage medium provided by the present application, there is a program product capable of implementing the time-frequency feature extraction method of the ultrasonic signal of the continuous casting round billet.

[0102] The time-frequency feature extraction method of the ultrasonic signal of the continuous casting round billet includes: selecting sampling points on the continuous casting round billet, collecting ultrasonic signals at the sampling points, and generating a CSV file for each sampling point; inputting the CSV file into MATLAB software, and preprocessing the ultrasonic signal by denoising and normalization; performing time-domain feature extraction and frequency-domain feature extraction on the preprocessed ultrasonic signal to obtain the time-domain feature extraction result and the frequency-domain feature extraction result; using MATLAB to generate an eigenvalue trend graph of the time-domain feature extraction result and the frequency-domain feature extraction result to visually analyze the result.

[0103] In summary, through a series of steps such as device parameter setting and data collection, MATLAB data preprocessing, time-domain and frequency-domain feature extraction and analysis, this method realizes the feature extraction of the ultrasonic waveform signal of the sampling sample, providing data support for subsequent related research or applications.

[0104] In some possible implementation manners, the time-frequency feature extraction method of the ultrasonic signal of the continuous casting round billet in the present disclosure can be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments described in the "Exemplary Method" section of this specification.

[0105] The storage medium of the present disclosure may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0106] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0107] For those of ordinary skill in the art, according to the teachings of the present invention, it does not require creative labor to design different forms of control circuits. These changes, modifications, substitutions, and variations to the embodiments still fall within the protection scope of the present invention without departing from the principles and spirit of the present invention.

Claims

1. A method for extracting time-frequency features of ultrasonic signals of continuous casting round billets, characterized in that: The method comprises: Select sampling points on the continuous casting round billet, collect ultrasonic signals at the sampling points, and generate a CSV file for each sampling point; The CSV file was input into MATLAB software to perform denoising and normalization preprocessing on the ultrasonic signal; Performing time domain feature extraction and frequency domain feature extraction on the preprocessed ultrasonic signal to obtain time domain feature extraction results and frequency domain feature extraction results; Use MATLAB to generate eigenvalue trend graphs of the time domain feature extraction results and the frequency domain feature extraction results to visualize the analysis results.

2. The method for extracting time-frequency features of ultrasonic signals according to claim 1, characterized in that: Select sampling points on the continuous casting round billet and collect ultrasonic signals at the sampling points, including: The ultrasonic wave is emitted to the continuous casting round billet by an ultrasonic generator; A plurality of ultrasonic probes are installed in the circumferential direction of the continuous casting round billet, and the ultrasonic probes are connected to an ultrasonic generator; The ultrasonic probe collects the ultrasonic signal reflected by the continuous casting round billet.

3. The method for extracting time-frequency features of ultrasonic signals according to claim 2, characterized in that: Before collecting the ultrasonic signal of the continuous casting round billet, it also includes: Along the circumference of the continuous casting round billet, each Install an ultrasonic probe; Adjust the parameters of the ultrasonic probe, including: probe frequency, sampling depth, transmitting voltage, number of sampling points, sampling frequency, calibration sound velocity, and probe delay.

4. The method for extracting time-frequency features of ultrasonic signals according to claim 3, characterized in that: The CSV file contains the following data: Sampling time interval T, sampling interval distance S, number of sampling points P, amplitude L1.

5. The method for extracting time-frequency features of ultrasonic signals according to claim 1, characterized in that: The ultrasonic signal is preprocessed by denoising and normalization, including the following specific steps: The ultrasonic signal is modulated using a linear frequency modulation signal: (1) In the formula, Indicates ultrasonic signal, represents the modulated ultrasonic signal, represents the imaginary part of a complex number, represents the Fourier rotation time, represents pi, represents the Fourier rotation angle; Convolve the modulated ultrasonic signal with another linear frequency modulated signal: (2) In the formula, represents the first-order time domain signal, represents the first-order convolution time domain signal; Again, the linear frequency modulation signal is used to To modulate: (3) In the formula, represents the normalized time domain signal.

6. The method for extracting time-frequency features of ultrasonic signals according to claim 4, characterized in that: Before extracting time domain features and frequency domain features from the preprocessed ultrasonic signal, it also includes: Define the characteristics of the time domain and frequency domain.

7. The method for extracting time-frequency features of ultrasonic signals according to claim 6, characterized in that: The pre-processed ultrasonic signal is subjected to time domain feature extraction and frequency domain feature extraction to obtain time domain feature extraction results and frequency domain feature extraction results, including: Time domain feature extraction: Use a loop statement to traverse all samples and extract time domain feature values, which include: mean value, standard deviation, skewness, kurtosis, maximum value, minimum value, peak-to-peak value, root mean square, crest factor, waveform factor, impulse factor, and energy; Generate a features array containing the time domain feature values; Frequency domain feature extraction: Convert the time domain sample data to the frequency domain through fast Fourier transform; Defining frequency domain eigenvalues ​​according to a preset frequency domain eigenvalue definition method; Complete the data analysis of the frequency domain eigenvalues ​​according to the preset analysis method and generate the frequency domain eigenvalue array; The average value refers to the average level of ultrasonic signal strength, and the calculation formula is: ； The standard deviation refers to the stability of the ultrasonic signal and is calculated as: ； Skewness refers to the symmetry of the signal distribution and is calculated as: ； Kurtosis refers to the shape of the signal distribution and is calculated as: ； Maximum It refers to the peak amplitude of the signal; Minimum It refers to the peak-to-valley amplitude of the signal; The peak-to-peak value refers to the entire amplitude range of the signal and is calculated as: ； The RMS refers to the energy level of the signal and is calculated as: ； Energy refers to the distribution of the signal at different frequency components, and the calculation formula is: ； The crest factor refers to the strength level of the signal and is calculated as: ； The form factor refers to the flatness or sharpness of the signal and is calculated as: ； The impulse factor refers to the transient or impulse characteristics in the signal, and the calculation formula is: 。 8. An ultrasonic signal time-frequency feature extraction system applied to the ultrasonic signal time-frequency feature extraction method according to any one of claims 1 to 7, characterized in that: The system comprises: Sampling module: used to select sampling points on the continuous casting round billet, collect ultrasonic signals at the sampling points, and generate CSV files for each sampling point; Processing module: used to input CSV files into MATLAB software to perform denoising and normalization preprocessing on ultrasonic signals; Analysis module: used to extract time domain features and frequency domain features of the preprocessed ultrasonic signal to obtain time domain feature extraction results and frequency domain feature extraction results; Display module: Use MATLAB to generate eigenvalue trend graphs of time domain feature extraction results and frequency domain feature extraction results to visualize the analysis results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for extracting time-frequency features of ultrasonic signals as described in any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for extracting time-frequency features of ultrasonic signals as described in any one of claims 1 to 7 are implemented.

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